_base_ = "../_base_/default_runtime.py"

# model settings
model = dict(
    type="RetinaNet",
    pretrained="open-mmlab://detectron2/resnet101_caffe",
    backbone=dict(
        type="ResNet",
        depth=101,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type="BN", requires_grad=False),
        norm_eval=True,
        style="caffe",
    ),
    neck=dict(
        type="FPN",
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs=True,
        num_outs=5,
    ),
    bbox_head=dict(
        type="GARetinaHead",
        num_classes=80,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        approx_anchor_generator=dict(
            type="AnchorGenerator",
            octave_base_scale=4,
            scales_per_octave=3,
            ratios=[0.5, 1.0, 2.0],
            strides=[8, 16, 32, 64, 128],
        ),
        square_anchor_generator=dict(
            type="AnchorGenerator",
            ratios=[1.0],
            scales=[4],
            strides=[8, 16, 32, 64, 128],
        ),
        anchor_coder=dict(
            type="DeltaXYWHBBoxCoder",
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0],
        ),
        bbox_coder=dict(
            type="DeltaXYWHBBoxCoder",
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0],
        ),
        loc_filter_thr=0.01,
        loss_loc=dict(
            type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0
        ),
        loss_shape=dict(type="BoundedIoULoss", beta=0.2, loss_weight=1.0),
        loss_cls=dict(
            type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0
        ),
        loss_bbox=dict(type="SmoothL1Loss", beta=0.04, loss_weight=1.0),
    ),
)
# training and testing settings
train_cfg = dict(
    ga_assigner=dict(
        type="ApproxMaxIoUAssigner",
        pos_iou_thr=0.5,
        neg_iou_thr=0.4,
        min_pos_iou=0.4,
        ignore_iof_thr=-1,
    ),
    ga_sampler=dict(
        type="RandomSampler",
        num=256,
        pos_fraction=0.5,
        neg_pos_ub=-1,
        add_gt_as_proposals=False,
    ),
    assigner=dict(
        type="MaxIoUAssigner",
        pos_iou_thr=0.5,
        neg_iou_thr=0.5,
        min_pos_iou=0.0,
        ignore_iof_thr=-1,
    ),
    allowed_border=-1,
    pos_weight=-1,
    center_ratio=0.2,
    ignore_ratio=0.5,
    debug=False,
)
test_cfg = dict(
    nms_pre=1000,
    min_bbox_size=0,
    score_thr=0.05,
    nms=dict(type="nms", iou_threshold=0.5),
    max_per_img=100,
)
# dataset settings
dataset_type = "CocoDataset"
data_root = "data/coco/"
img_norm_cfg = dict(mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(
        type="Resize",
        img_scale=[(1333, 480), (1333, 960)],
        keep_ratio=True,
        multiscale_mode="range",
    ),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="Pad", size_divisor=32),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=data_root + "annotations/instances_train2017.json",
        img_prefix=data_root + "train2017/",
        pipeline=train_pipeline,
    ),
    val=dict(
        type=dataset_type,
        ann_file=data_root + "annotations/instances_val2017.json",
        img_prefix=data_root + "val2017/",
        pipeline=test_pipeline,
    ),
    test=dict(
        type=dataset_type,
        ann_file=data_root + "annotations/instances_val2017.json",
        img_prefix=data_root + "val2017/",
        pipeline=test_pipeline,
    ),
)
evaluation = dict(interval=1, metric="bbox")
# optimizer
optimizer = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
    policy="step",
    warmup="linear",
    warmup_iters=500,
    warmup_ratio=1.0 / 3,
    step=[16, 22],
)
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
    interval=50,
    hooks=[
        dict(type="TextLoggerHook"),
        # dict(type='TensorboardLoggerHook')
    ],
)
# yapf:enable
# runtime settings
runner = dict(type="EpochBasedRunner", max_epochs=24)
